These were all the libraries used to help with data exploration of the NBA data set.

library(dplyr)
library(ggplot2)
library(tidyr)
library(stringr)
library(data.table)
library(ggrepel)
library(directlabels)
library(gridExtra)
options(max.print = 999999999)
options(scipen=12)
## [1] "/Users/justinvhuang/Desktop/nba_stat_salaries"
## [1] "/Users/justinvhuang/Desktop/nba_stat_salaries"

Live and Die by the Three

The first thing that we should explore as a NBA team looking to have a succesful season in the modern era is to see the increased number of 3 point shots. How the NBA team according to many news articles has evolved into a spacing oriented and 3 point focused game during the regular season.

Let us first look at the distrubtion of the 3 point shot made, 2 point shot made and salary.

##   median(salary) mean(salary) sd(salary)    var(salary) IQR(salary)
## 1        2678400      4523495    4862128 23640286672352     4899590

There seems to be a right skew in the data. Showing that superstars and stars get the biggest pay day and take the biggest proportion of the teams salary.

##   median(two) mean(two)  sd(two) var(two) IQR(two)
## 1         2.1  2.667674 1.899015  3.60626      2.5

##   median(three) mean(three) sd(three) var(three) IQR(three)
## 1           0.3   0.5867761 0.6674279  0.4454601          1

Both are right skewed showing that the stars and main players on the team usually take most of the shots.

Lets now look at the trend over the years of the three point shot vs two point shot.

The mean 3 point shots have been increasing indicated by the trending upward blue dots. While the number of 2 point shots have been decreasing by the red dots going down.

For players who took more than three 3’s a game there is an increase in salary after the 2015 year. However, there is still a decrease before then. Perhaps teams didn’t pay big salaries for 3 point specialist earlier on and emphasized on other basketball statistics.

Let us explore if 3 pointers lead to more wins

2014 seems to be an increase in wins for taking more than three 3’s a game. Before that the league was more big man focused with players such as Kevin Garnett, Tim Duncan, Shaquille O’neal, Jermaine O’neal, Dwight Howard.

Now let us explore the other end of the spectrum. With less than three 3’s a game.

There is still an increase in salary. This is due to the TV contract money with the NBA increasing the salary cap which was a huge factor. However, if you look at the summary of the mins and max you can see taking more than 3 threes a game is beneficial.

Attemping more 3’s is benefical it looks like. From the years 2004 to 2008 the league still had traditional big men who dominated the league. Spacing and rules didn’t emphasize spacing as much. Having players that take less than three 3’s a game leads to a team win that would be 8th seed in the east and out of the playoffs in the western conference.

Making more two’s of course leads to a better salary. The TV money seems have a major factor to increase the salary for the players after 2015.

Conclusion:

Overall 3 point shooting does lead to more wins and an increase of salary. However, due to the TV contract there was a huge salary bump as well as new CBA and talks with the players association.

PPG vs EFG

With the increased reliance on data interpretation what we want to explore is the rise of EFG vs PPG. Players like Rudy Gay and Josh Smith who scored in bunches before used to be rewarded huge contracts. However, it was later found they scored inefficiently.

\[ EFG = (FG + 0.5 * 3P) / FGA. \]

Salary over the years has increased. But there doesn’t seem to be a clear relationship with points per game over the years and salary.

EFG seems to be trending in the later years. As more GMs and teams look at data they are paying more for players who are shooting more effectively. In the past teams perhaps were only looking at points per game.

Lets look at a 20 ppg scorer vs a 50 percent EFG players and see how their salaries compare.

Lets look at over 51 percent EFG

Early on EFG didn’t seem to take notice to teams as much, but as we entered the 2014 year and above this statisic became more relevant

Which schools in the US are teams spending their salary the most for salary.

Exploring this data could give us information on where to look to draft a NBA prospect for a NBA team.

##  [1] "Alabama"                  "Alabama A&M"             
##  [3] "Alabama-Birmingham"       "Alabama-Huntsville"      
##  [5] "American International"   "Arizona"                 
##  [7] "Arizona State"            "Arkansas"                
##  [9] "Arkansas-Little Rock"     "Auburn"                  
## [11] "Auburn-Montgomery"        "Augsburg"                
## [13] "Austin Peay"              "Ball State"              
## [15] "Barton Community College" "Baylor"                  
## [17] "Belmont"                  "Blinn"                   
## [19] "Boise State"              "Boston College"

249 schools (no school included for internationals if they played professionally outside the USA)

Lets look at the top 10 schools that got paid

## # A tibble: 11 x 3
##    school         count_school     salary
##    <fct>                 <int>      <dbl>
##  1 None                   1285 7407695183
##  2 Kentucky                261 1220596079
##  3 Duke                    252 1308479631
##  4 North Carolina          241 1182185814
##  5 UCLA                    206  947159451
##  6 Kansas                  194  840093830
##  7 Arizona                 190  995023686
##  8 Connecticut             184 1038212650
##  9 Florida                 143  817700613
## 10 Georgia Tech            129  718884989
## 11 Texas                   116  627431018

Should be always looking at these schools for prospects looking at the total salary paid. Seems to have best basketball programs

Lets take a look at the data that was accounted for by college programs

Looking at the notable names Lebron James, Kobe Bryant, Kevin Garnett, Dwight Howard, Jermaine O’neal that would be 5 out of 430 highschoolers who made it to superstar level. That’s 1.2 percent chance.

Out of Country Prospects for teams to spend their salary on.

Looking at the top salaries would want to look into these countries in terms if finding talent or drafting in the future or free agents.

How important is getting the number 1 pick by either saving salary by tanking or being unlucky

Now lets compare to being a relatively good team but paying for good scouts to draft or look abroad in the second round.

We can see that by not selecting the number one pick overall it yielded 4 more defensive players or nba team selections. But we have to keep in mind other picks in the first round. Other notable picks that aren’t first rounders include Stephen Curry, Kevin Durant, A’mare Stoudamire.

The Best vs the Worst

Lets look at the data for the winning team and team that lost the most for each year 2000 to 2018

Winning teams tend to spend over the cap while losing teams spend under or stay below the salary cap for each year.

Lets look at the Top teams each year vs Worst teams each year

We can see the top teams spend the most money each year. Their best players also recieve the more money than the worst teams player. Looking at the average salary the best teams spend much more than the worst.

Most successful teams in the last 19 years.

Looking at the team that spent the most over and the team that spent the least.

Looking at the winningest team vs the team that lost the most in the last 19 years.

The team that won the most is the 2016 GSW and the team with the fewest wins was the 2012 Charlotte Bobcats. The team that spent the most over the salary cap was the 2018 Cleveland Caviliers. The team that spent the least over the salary cap was the 2000 LA clippers.

## # A tibble: 14 x 6
## # Groups:   salary [14]
##    name                   salary   ppg   EFG t_reb PlusMinus
##    <fct>                   <int> <dbl> <dbl> <dbl>     <dbl>
##  1 Klay Thompson        15501000  22.1 0.569   3.8      14.7
##  2 Draymond Green       14260870  14   0.554   9.5      18  
##  3 Andrew Bogut         12000000   5.4 0.625   7        13.9
##  4 Andre Iguodala       11710456   7   0.544   4        13.2
##  5 Stephen Curry        11370786  30.1 0.631   5.4      17.7
##  6 Anderson Varejao     10158574   2.6 0.435   2.7       2.6
##  7 Shaun Livingston      5543725   6.3 0.531   2.2       7  
##  8 Harrison Barnes       3873398  11.7 0.531   4.9      10.5
##  9 Marreese Speights     3815500   7.1 0.452   3.3       1.2
## 10 Leandro Barbosa       2500000   6.4 0.519   1.7       2.4
## 11 Festus Ezeli          2008748   7   0.54    5.6      14.6
## 12 Brandon Rush          1270964   4.2 0.542   2.5      -0.9
## 13 Ian Clark              947276   3.6 0.5     1        -7.3
## 14 James Michael McAdoo   845059   2.9 0.55    1.4     -15.9
##      total  TopPaid    NameTopSal highscore    NameTopPpg efficient
## 1 95806356 15501000 Klay Thompson      30.1 Stephen Curry 0.6311881
##      NameTopEFG HighPlusMinus      NameTopPM LeastPaid
## 1 Stephen Curry            18 Draymond Green    845059
##             NameLowSal  AvgSal tmsalary   salcap OverUnder wins
## 1 James Michael McAdoo 6843311 93669566 70000000  1.338137   73

From the greenline we can see that team salary has stayed above salary cap.

Lets see how they drafted in the last 19 years

##                  name  salary  ppg       EFG
## 104    Antawn Jamison 2503800 19.6 0.4715909
## 1564 Jason Richardson 2607360 15.6 0.4612676
## 1746        JR Bremer  563679  3.3 0.3295455
## 4732    Stephen Curry 2913840 18.6 0.5492958
## 5358    Klay Thompson 2222160 16.6 0.5102041
## 5813  Harrison Barnes 2923920  9.5 0.4482759

They also successfully drafted Draymond Green who won defensive player of the year.

Looking at the worst team in NBA history

## # A tibble: 14 x 6
## # Groups:   salary [14]
##    name               salary   ppg   EFG t_reb PlusMinus
##    <fct>               <int> <dbl> <dbl> <dbl>     <dbl>
##  1 Corey Maggette   10262069  15   0.402   3.9     -15.4
##  2 Tyrus Thomas      7305765   5.6 0.361   3.7     -14.4
##  3 DeSagana Diop     6925400   1.1 0.375   3.1     -19.1
##  4 Matt Carroll      3900000   2.7 0.367   1.1     -11.9
##  5 DJ Augustin       3236470  11.1 0.441   2.3     -14.7
##  6 Bismack Biyombo   2798040   5.2 0.455   5.8     -16  
##  7 Eduardo Najera    2750000   2.6 0.448   2.3      -7.2
##  8 Reggie Williams   2500000   8.3 0.481   2.8     -18.4
##  9 Kemba Walker      2356320  12.1 0.414   3.5     -14.3
## 10 Gerald Henderson  2250600  15.1 0.466   4.1     -13.7
## 11 DJ White          2001167   6.8 0.492   3.6     -15.3
## 12 Byron Mullens     1288200   9.3 0.440   5       -12.4
## 13 Derrick Brown      854389   8.1 0.532   3.6     -13  
## 14 Cory Higgins       473604   3.9 0.345   0.9     -12.9
##      total  TopPaid     NameTopSal highscore       NameTopPpg efficient
## 1 48902024 10262069 Corey Maggette      15.1 Gerald Henderson  0.531746
##      NameTopEFG HighPlusMinus      NameTopPM LeastPaid   NameLowSal
## 1 Derrick Brown          -7.2 Eduardo Najera    473604 Cory Higgins
##    AvgSal tmsalary   salcap OverUnder wins
## 1 3493002 57902024 58044000  0.997554    7

You can see Michael Jordan didn’t really spend that much money on his bobcats to have them winning.

Comparing the worst vs the best team side by side

The GSW looked as if they spent double the amount. But also have to include the salary cap increase. Teams with winning players will spend more over the cap than teams who can’t win. The real question is do teams spend just enough to stay in cap and then just tank to get draft picks and then players?

Looking at the max salary offender

##      team PlayerYear
## 7508  CLE       2018
## 7509  CLE       2018
## 7510  CLE       2018
## 7514  CLE       2018
## 7520  CLE       2018
## 7538  CLE       2018
## 7539  CLE       2018
## 7552  CLE       2018
## 7772  CLE       2018
## 7773  CLE       2018
## 7774  CLE       2018
## 7775  CLE       2018
## 7776  CLE       2018
## 7777  CLE       2018
## 7778  CLE       2018

##       total  TopPaid   NameTopSal highscore   NameTopPpg efficient
## 1 138780646 33285709 LeBron James      27.5 LeBron James 0.7142857
##   NameTopEFG HighPlusMinus   NameTopPM LeastPaid   NameLowSal  AvgSal
## 1 Ante Zizic           7.9 Kyle Korver     77250 John Holland 9252043
##    tmsalary   salcap OverUnder wins
## 1 137722926 99093000  1.389835   50
##     team PlayerYear
## 179  LAC       2000
## 180  LAC       2000
## 181  LAC       2000
## 182  LAC       2000
## 183  LAC       2000
## 184  LAC       2000
## 185  LAC       2000
## 186  LAC       2000
## 187  LAC       2000
## 217  LAC       2000
## 256  LAC       2000
## 258  LAC       2000
## 259  LAC       2000
## 281  LAC       2000
## 282  LAC       2000
## # A tibble: 15 x 6
## # Groups:   salary [12]
##    name                salary   ppg   EFG t_reb PlusMinus
##    <fct>                <int> <dbl> <dbl> <dbl>     <dbl>
##  1 Michael Olowokandi 3456240   9.8 0.432   8.2     -13.8
##  2 Tyrone Nesby       2716667  13.3 0.452   3.8     -13.2
##  3 Lamar Odom         2445480  16.6 0.467   7.8      -7.5
##  4 Eric Piatkowski    2000000   8.7 0.5     3       -12.6
##  5 Eric Murdock       1925000   5.6 0.431   1.9     -10.3
##  6 Keith Closs        1680000   4.2 0.486   3.1     -10.6
##  7 Derek Anderson     1439400  16.9 0.474   4       -13.1
##  8 Maurice Taylor     1367400  17.1 0.465   6.5     -12.8
##  9 Brian Skinner       843000   5.4 0.512   6.1      -6.6
## 10 Anthony Avent       510000   1.7 0.3     1.5      -8.3
## 11 Pete Chilcutt       510000   2.1 0.413   2.3     -12.1
## 12 Troy Hudson         460000   8.8 0.437   2.4     -10.9
## 13 Etdrick Bohannon    460000   2.2 0.5     2.4     -13.6
## 14 Jeff McInnis        460000   7.2 0.453   2.9     -13.3
## 15 Charles R Jones     385000   3.4 0.431   1.1     -12.1

##      total TopPaid         NameTopSal highscore     NameTopPpg efficient
## 1 20658187 3456240 Michael Olowokandi      17.1 Maurice Taylor 0.5121951
##      NameTopEFG HighPlusMinus     NameTopPM LeastPaid      NameLowSal
## 1 Brian Skinner          -6.6 Brian Skinner    385000 Charles R Jones
##    AvgSal tmsalary   salcap OverUnder wins
## 1 1377212 22489343 34000000 0.6614513   15
##                    name  salary  ppg       EFG
## 186  Michael Olowokandi 3456240  9.8 0.4315789
## 775          Lamar Odom 2628960 17.2 0.4963768
## 1059       Darius Miles 3054840  9.5 0.4871795
## 3942        Eric Gordon 2623200 16.1 0.5301724
## 3943        Al Thornton 1776240 16.8 0.4560811
## 4095        Eric Gordon 2819880 16.9 0.5277778
## 5085       Eric Bledsoe 1596360  3.3 0.4062500
## 5123      Blake Griffin 5731080 20.7 0.5483871

The Clippers drafted well with Blake Griffin however there were many injuries. So tanking to get a star was worth it. They also got Eric Gordon and Lamar Odom who both woth 6th man of the year.

Comparing them with the Cavs. We can see the Cavs spent money to keep their team intact for their Finals run.

Comparing the Average Joe

##      height   weight  salary  plusMinus      age    games    start
## 1  200.9356 222.0764 2880067 -1.1627685 27.74702 58.84248 28.57518
## 2  200.9545 222.6364 3398885 -1.2510101 27.72727 59.10859 29.96465
## 3  201.5609 224.6878 3487920 -0.9461929 27.18782 58.99239 29.94162
## 4  201.6292 224.7008 3783693 -0.9245524 27.12788 60.20716 30.38875
## 5  201.3472 224.0000 3817195 -0.9489637 27.13731 59.93782 30.67098
## 6  201.4734 224.2488 3911320 -1.5695652 26.94928 58.76570 29.57729
## 7  201.0049 223.2469 4091303 -1.2205379 26.54279 59.48166 30.03912
## 8  200.6675 222.7530 4140794 -1.2315914 26.44893 58.72922 29.19002
## 9  200.8610 222.3325 4574357 -1.1846154 26.84367 59.01985 29.46402
## 10 201.2897 223.0907 4715669 -1.4602015 26.57683 59.11839 30.14106
## 11 200.9824 222.8643 4829387 -1.1979899 26.63065 59.36181 29.98744
## 12 201.3957 224.0192 4560710 -1.2580336 26.63309 59.46043 29.47242
## 13 200.9750 222.8636 4348460 -1.7250000 26.58182 46.56591 22.44545
## 14 200.8822 222.1455 4447435 -1.1697460 26.74134 58.67206 28.35566
## 15 200.9120 221.4718 4402763 -1.0440181 26.56208 56.99097 27.74266
## 16 200.8978 221.3244 4430783 -1.5308889 26.68667 56.22222 26.72444
## 17 201.1982 221.5248 5097982 -1.4074324 26.71171 57.95045 27.69369
## 18 201.1186 220.4295 6218880 -1.3767338 26.44743 57.69128 27.50336
## 19 200.5735 218.2668 6450838 -1.6067227 26.18697 54.02731 25.83193
##        mins       fg   fg_att    fg_per     three three_att three_per
## 1  21.36683 3.163962 7.130549 0.4365386 0.4152745  1.196897 0.3469591
## 2  22.04369 3.172980 7.226768 0.4314876 0.4270202  1.214646 0.3515593
## 3  22.13680 3.268528 7.395939 0.4361455 0.4573604  1.315990 0.3475410
## 4  21.92558 3.178517 7.233504 0.4312024 0.4498721  1.297954 0.3466010
## 5  22.43523 3.219689 7.393523 0.4314557 0.4694301  1.368135 0.3431168
## 6  22.23430 3.242995 7.316908 0.4368336 0.4985507  1.423430 0.3502461
## 7  22.09927 3.220049 7.142787 0.4449107 0.5051345  1.422005 0.3552270
## 8  21.78147 3.243468 7.123278 0.4489531 0.5399050  1.519952 0.3552118
## 9  21.88462 3.283623 7.260794 0.4452385 0.5727047  1.604963 0.3568336
## 10 22.30428 3.368766 7.394207 0.4500620 0.5881612  1.620907 0.3628594
## 11 22.20503 3.392211 7.397990 0.4549261 0.5753769  1.642714 0.3502600
## 12 21.86355 3.294005 7.226859 0.4536585 0.5693046  1.598801 0.3560822
## 13 21.37068 3.148636 7.088636 0.4374295 0.5572727  1.616364 0.3447694
## 14 21.42471 3.220785 7.195150 0.4430161 0.6217090  1.771824 0.3508863
## 15 21.31603 3.255305 7.228217 0.4441381 0.6706546  1.890745 0.3547039
## 16 21.10867 3.213556 7.233111 0.4399928 0.6842222  1.974444 0.3465391
## 17 21.10946 3.279054 7.300450 0.4472961 0.7204955  2.056081 0.3504217
## 18 20.98277 3.321477 7.314989 0.4507245 0.8192394  2.313423 0.3541244
## 19 20.86660 3.341807 7.363445 0.4518979 0.8915966  2.509244 0.3553248
##         two  two_att   two_per       efg       ft   ft_att    ft_per
## 1  2.751551 5.931981 0.4533731 0.4636205 1.614797 2.168258 0.7447441
## 2  2.744192 6.006566 0.4447630 0.4589386 1.648737 2.220202 0.7426069
## 3  2.808376 6.078426 0.4528598 0.4633263 1.612183 2.154569 0.7482625
## 4  2.725064 5.936829 0.4467322 0.4589927 1.635806 2.171355 0.7533569
## 5  2.749223 6.030570 0.4465291 0.4599201 1.655181 2.217098 0.7465529
## 6  2.745652 5.896135 0.4558527 0.4670815 1.766184 2.355556 0.7497949
## 7  2.713692 5.719560 0.4639777 0.4762883 1.755990 2.367726 0.7416357
## 8  2.705226 5.605463 0.4710350 0.4836586 1.746793 2.325653 0.7510979
## 9  2.708189 5.654839 0.4662997 0.4822767 1.661042 2.228040 0.7455173
## 10 2.779597 5.770277 0.4695675 0.4879611 1.702519 2.234509 0.7619209
## 11 2.816834 5.754020 0.4830450 0.4916816 1.676131 2.223367 0.7538705
## 12 2.724460 5.628537 0.4743616 0.4914891 1.623501 2.148681 0.7555804
## 13 2.590455 5.472045 0.4634083 0.4749558 1.461364 1.953636 0.7480223
## 14 2.603233 5.422171 0.4720255 0.4849253 1.447575 1.937644 0.7470799
## 15 2.584424 5.334989 0.4844292 0.4899395 1.517156 2.014221 0.7532220
## 16 2.530889 5.258444 0.4724925 0.4866008 1.461333 1.960222 0.7454937
## 17 2.559910 5.245045 0.4820120 0.4949224 1.518919 2.022973 0.7508350
## 18 2.503803 5.000447 0.4925256 0.5044656 1.513199 1.970470 0.7679382
## 19 2.448950 4.857143 0.4964841 0.5103584 1.409874 1.842437 0.7652223
##        o_reb    d_reb    t_reb   assist     steal     block       TO
## 1  1.1193317 2.684964 3.805489 1.926969 0.6947494 0.4441527 1.308115
## 2  1.1022727 2.782576 3.886616 1.927273 0.7131313 0.4750000 1.305303
## 3  1.1390863 2.761929 3.899239 1.955838 0.7104061 0.4794416 1.269543
## 4  1.1153453 2.731202 3.843223 1.909719 0.7150895 0.4659847 1.291816
## 5  1.1341969 2.789637 3.920466 1.946891 0.7300518 0.4608808 1.335233
## 6  1.1128019 2.739372 3.853382 1.914010 0.6917874 0.4492754 1.279710
## 7  1.0273839 2.715648 3.739120 1.831051 0.6562347 0.4283619 1.264792
## 8  1.0049881 2.682423 3.686698 1.876960 0.6539192 0.4121140 1.311639
## 9  1.0317618 2.776427 3.806700 1.949132 0.6625310 0.4344913 1.232506
## 10 1.0423174 2.806297 3.847355 1.875819 0.6690176 0.4632242 1.237028
## 11 1.0288945 2.821608 3.848995 1.907035 0.6600503 0.4555276 1.243970
## 12 1.0074341 2.775300 3.779376 1.876499 0.6565947 0.4510791 1.229976
## 13 1.0027273 2.702045 3.701364 1.835682 0.6779545 0.4477273 1.238182
## 14 0.9921478 2.727945 3.721247 1.934180 0.6866051 0.4533487 1.228176
## 15 0.9629797 2.783070 3.747178 1.915124 0.6717833 0.4164786 1.249661
## 16 0.9446667 2.802889 3.741111 1.905333 0.6786667 0.4097778 1.198889
## 17 0.9121622 2.896622 3.803153 1.929505 0.6826577 0.4344595 1.209459
## 18 0.8870246 2.876957 3.762640 1.930425 0.6657718 0.4105145 1.157271
## 19 0.8352941 2.870798 3.704202 1.993277 0.6739496 0.4115546 1.181513
##       fouls      ppg PlayerYear
## 1  2.126730 8.356086       2000
## 2  2.092424 8.421970       2001
## 3  1.990609 8.605330       2002
## 4  2.036829 8.443223       2003
## 5  2.034197 8.566062       2004
## 6  2.151691 8.751691       2005
## 7  2.141565 8.699022       2006
## 8  2.047743 8.775534       2007
## 9  1.960794 8.801737       2008
## 10 1.987406 9.029723       2009
## 11 1.983668 9.028141       2010
## 12 1.943405 8.773621       2011
## 13 1.779545 8.311364       2012
## 14 1.800231 8.513395       2013
## 15 1.869074 8.694808       2014
## 16 1.807778 8.568444       2015
## 17 1.811486 8.797523       2016
## 18 1.768680 8.970694       2017
## 19 1.753782 8.973319       2018

##     name   height   weight  salary plusMinus      age    games   start
## 1 AvgJoe 201.0873 222.5623 4399392 -1.274556 26.81424 57.84977 28.6163
##      mins      fg   fg_att    fg_per     three three_att three_per
## 1 21.7084 3.25418 7.261427 0.4429425 0.5806992  1.650448 0.3515404
##        two two_att   two_per       efg       ft   ft_att    ft_per
## 1 2.673354 5.61071 0.4679881 0.4806002 1.601489 2.132454 0.7511976
##      o_reb    d_reb    t_reb   assist    steal     block       TO    fouls
## 1 1.021201 2.775143 3.794608 1.912669 0.681629 0.4422839 1.251199 1.951981
##       ppg
## 1 8.68851

Above is the chart of taking all the players and averaging them all out to create Mr. Avg Joe. Now lets compare him to differnt categories of NBA players.

What is suprising from the data is the height didn’t really change. However, perhaps the average height increased so there are more people 201cm in the league.

Lets see maybe the smallest player got taller and on average all nba players are the same height

Clearly there are still a lot of players who are 213 cm and over there was even a small increase in the year 2017.

Average Joe vs Superstar, Star and Roleplayer

SuperStars

Looking at the life cycle of a NBA player we can determine a salary reference point of how to pay them.

Duncan salary and production is above the average player. Late in his career he took pay cuts which brought his salary down.

Kobe commanded a much higher salary than the avg Joe. He also did not take a decrease in salary for the later years. However his minutes per game did not go down. It might be interesting to do an analysis on his achillies injury and overuse of his body in future analysis.

Lebron salary has kept going up. His body has not slowed down either. The amount of minutes played has not really decreased at all. Seems like a player anomally.

Garnett seems to have taken a few paycuts for the Timberwolves to get him better players. Production did go down a lot. Perhaps bigmen have smaller life cycles than smaller players.

Dirk took pay cuts as well. His production also started to decrease. It would be interesting to compare height and player life cycle.

Taller players that make it past year 10 seem to have a longer life cycle.

Stars

Vince Carters Life cycle goes up in years where it reaches its peak in his 30s then begins to decrease. Pay goes up because of new contract deals for NBA players increasing the overall salary cap.

Manu had a decrease in salary pay as he passed 35. But as soon as the contract money got signed the new TV deal he got paid due to an increase in salary cap overall.

There is no decreasein salary for Melo. However, this player is out of the league now due to the ineffciency.

Gasol also had a decrease in salary but the new TV money seems to have set in a new reference point on how much veterans should be paid.

Parkers salary went down but again due to the new TV contract money the salary did increase back up again even though the player was aging.

RolePlayer

Nene salary decreased. Probably got the veterans minimum with the new 2017 CBA.

Mike Miller took a pay cut probably to play for a contender.

Dunleavy had a pay decrease then a small increase because of the salary bump overall.

Jamal Crawford got a huge payday after the new TV money after an initial decrease.

Fisher never made it to the new TV contract money. His salary follows a normal trend according to his age.